In water treatment and supply, it may be desirable, e.g. for security reasons, to regularly monitor the water for contamination. One method that is rapidly gaining prominence in such drinking water security applications comprises the use of living organisms or enzymatic reactions as toxicity indicators. By monitoring adverse biological effects on test organisms, it is possible to confirm the presence of toxins in the water supply prior to performing further analysis to determine the exact nature of the threat. The key advantages of using bio-monitoring are its rapid response and its ability to be used at critical locations, such as at the downstream of a water distribution system, prior to biological processing in a waste-water treatment plant. There are several different organisms that can be used to monitor for toxicity including e.g. bacteria, invertebrates and fish.
In early systems, such monitoring of the status of the test organisms is carried out by human inspectors. It has been noted that manual monitoring round-the-clock is tedious and subject to error since human alertness may degrade after a prolonged period, while periodic monitoring does not provide real-time information for appropriate responses. A computer vision system may be more suitable in this situation as the machine can provide constant monitoring over a long period of time at the same level of performance.
In one prior art approach, a video camera is used to visually monitor the movements of the fish. A behavioral model based on a set of statistical distributions of pre-selected movement parameters is computed as a time function. The approach further comprises comparing observed movement statistical distribution with a set of prediction parameters, and generating a warning message when the fish' observed movements do not correspond to prediction parameters. However, as the prediction parameters are dependent on the previously observed behavior, this approach may not be responsive to situations subject to different types of fish and different contaminant exposures that have not been observed and recorded in the historical database.
In another prior art approach, a monitoring area is divided into a plurality of small blocks (i.e. sensing points) in rows and columns and the luminance value of each block is checked to determine whether the block is occupied by a fish or not. The blocks being occupied by a fish are labeled such that an analysis of fish action pattern may be performed by calculating the number of labeled blocks, moving speed, etc. In this approach, the fish tank needs to be divided into a number of confinement structures, each containing one fish. As a result, the action pattern may not be accurate as compared to a natural setting in which a plurality of fish may swim in a group.
In one existing system, water is passed through a transparent tank having a substantially narrow width, and containing a plurality of fish. A video camera is directed toward the front side of the tank, i.e. the larger side defined by the length and height of the tank. The system measures behavioral parameters such as speed, behaviors (e.g. in terms of height in tank, turns and circular motion), size and number of active fish. Toxicity is measured as global statistics of these behavioral parameters. However, the system is not able to resolve occlusions, i.e. situations where one fish is partially or fully obstructed by one or more fish positioned nearer to the camera. As the analysis of fish activity is based on global motion statistics, the analysis may be inconclusive in instances with just one or two dead fish. In addition, due to the very narrow width, it is not a natural setting for keeping the fish. This could lead to the fish not being able to move sufficiently fast, thus may be detected as dead fish, resulting in false alarms.
Another existing water quality monitoring system comprises a receiving tank and a monitoring tank, which may have up to two sections, each containing one fish. A picture signal from a video camera positioned at one side of the monitoring tank is inputted into a fish detecting sensor where the picture signal is processed to continuously detect the position of the fish. A position signal is fed into a computer for determining the actional pattern of the fish, which is then compared with a fish abnormal actional pattern stored in advance to check whether the actional pattern observed is abnormal or not. However, like the above approaches, the number of fish being monitored is limited, and the actional pattern may not be accurate as compared to a natural setting in which a plurality of fish may swim in a group.
A need therefore exists to provide a system and method for monitoring water quality that seek to address at least one of the above problems.